Spinal Dural Repair: A Canadian Questionnaire
Bibliographic record
Abstract
STUDY DESIGN: Questionnaire. OBJECTIVES: Iatrogenic dural tear is a complication of spinal surgery with significant morbidity and cost to the health care system. The optimal management is unclear, and therefore we aimed to survey current practices among Canadian practitioners. METHODS: A questionnaire was administered to members of the Canadian Neurological Surgical Society designed to explore methods of closure of iatrogenic durotomy. RESULTS: Spinal surgeons were surveyed anonymously with a 55% response rate (n = 91). For pinhole-sized tears, there is no agreement in the methods of closure, with a trend toward sealant fixation (36.7%). Medium- and large-sized tears are predominantly closed with sutures and sealant (67% and 80%, respectively). Anterior tears are managed without primary closure (40.2%), or using sealant alone (48%). Posterior tears are treated with a combination of sutures and sealant (73.8%). Nerve root tears are treated with either sealant alone (50%), or sutures and sealant (37.8%). Tisseal is the preferred sealant (79.7%) over alternatives. With the exception of pin-hole sized tears (39.5%) most respondents recommended bed rest for at least 24 hours in the setting of medium (73.2%) and large (89.1%) dural tears. CONCLUSIONS: This study elucidates the areas of uncertainty with regard to iatrogenic dural tear management. There is disagreement regarding management of anterior and nerve root tears, pinhole-sized tears in any location of the spine, and whether patients should be admitted to hospital or should be on bed rest following a pinhole-sized dural tear. There is a need for a robust comparative research study of dural repair strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".